People with diabetes, especially those suffering from Type 1 or juvenile diabetes, ought to measure their glucose levels frequently in order to be able to adjust treatment or behaviour to maximise time spent in normoglycaemia. Traditionally, glucose levels are measured manually by means of specialised glucose monitoring equipment comprising a lancet, glucose test strips and a dedicated metering device. During one such measurement the user punctuates the skin, typically on a finger, and obtains a small drop of blood which is placed onto a test strip. The test strip is then read by the blood glucose meter and after a few seconds the meter displays the result. The finger lancing can be quite painful and to repeat this procedure multiple times during the day is highly undesirable. Furthermore, since the user has to use, and bring around, three different system parts in order to carry out a measurement this form of glucose monitoring is viewed as a nuisance by most people with diabetes.
Recent advances in sensor technology have led to the development of wearable continuous glucose monitoring systems, also known as CGM systems, which are able to measure and display tissue glucose levels continuously (or near-continuously). These systems generally comprise a skin adhesive patch carrying a small sensor adapted for percutaneous placement, a sensor insertion applicator, wireless communication means, and a hand-held remote receiver device capable of interpreting the sensor signals and presenting the results. The sensors can be used for five to seven days and are subsequently discarded. In the course of these five to seven days the sensors need only be calibrated (using blood glucose measurements obtained manually) a couple of times per day or less, depending on the particular sensor brand.
CGM systems prospectively offer superior user convenience in comparison with conventional blood glucose monitoring equipment, partly because of the reduced requirement for performing painful and cumbersome fingerstick measurements, and partly because the measurements are performed automatically and processed continuously, thereby ensuring that dangerous glucose excursions are detected and the user is alerted to them in time. However, the currently marketed systems are only cleared for use in conjunction with conventional blood glucose testing and so the manual glucose testing is in principle not much reduced.
Furthermore, even though it is possible for glucose monitoring systems to provide real-time test results it remains desirable to more reliably predict glucose level fluctuations in the near future, e.g. half an hour or an hour ahead.
Estimation of future glucose concentrations is a crucial task for diabetes management since a projected picture of the glycaemic state of a person will be an invaluable help in relation to minimising glucose excursions and avoiding dangerous hypoglycaemic events. Continuous glucose monitoring provides detailed insight into glucose variations, and several methods have been developed recently for glucose prediction from CGM data, e.g. as presented in Sparacino et al.: “Glucose concentration can be predicted ahead in time from continuous glucose monitoring sensor time-series”, IEEE Trans. on Biomedical Eng., 54(5): 931-937, 2007, Reifman et al.: “Predictive Monitoring for Improved Management of Glucose Levels”, Journal of Diabetes Sci. and Tech., 1(4): 478-486, 2007, Zanderigo et al.: “Glucose prediction algorithms from continuous monitoring data: Assessment of accuracy via Continuous Glucose Error-Grid Analysis”, Journal of Diabetes Sci. and Tech., 1(5): 645-651, 2007, and Eren-Oruklu et al. “Estimation of future glucose concentrations with subject-specific recursive linear models”, Diabetes Technology & Therapeutics, 11(4): 243-253, 2009.
All these methods are based on time-series identification methodology and differ only in type and complexity of identified time-series models such as polynomial models, autoregressive models (AR), autoregressive moving average (ARMA) models, or other models from the MATLAB system Identification Toolbox.
In essence, in a model of a fixed type, model parameters are fitted at each sampling time against past glucose data. Then, the fitted model is iteratively used to predict the glucose level for a given prediction horizon (PH).
In a number of aspects time-series models appear rigid and in practice less suitable for the purpose of predicting future glucose concentrations, e.g. because such models need both frequent and consistent data input. This entails a high involvement of the user, e.g. via frequent glucose testing operations, and/or a need for an automatic glucose monitoring apparatus which is capable of conveying sampled data frequently and in a highly reliable manner. Therefore, from a user convenience point of view it is desirable to develop a glucose prediction method which neither requires a high sampling rate nor regularly sampled data.
There are several publications in the patent literature disclosing diabetes management systems, which comprise patient operated apparatuses programmed to predict the patient's future blood glucose values. A high reliability of the prediction is crucial for all such systems. In WO 2005/041103 an improvement of the reliability is achieved by providing a plurality of mathematical models, each adapted to generate a respective prediction from the same input. It is desirable that this plurality of mathematical models comprises at least two models based on different approaches.
At the moment all known and justified prediction models in CGM systems are based on a time-series approach or linear extrapolation. Even more, as also mentioned in Kovatchev and Clarke: “Peculiarities of the Continuous Glucose Monitoring Data Stream and Their Impact on Developing Closed-Loop Control Technology”, Journal of Diabetes Sci. and Tech., 2(1): 158-163, 2008, in CGM systems practically all predictions are currently based on a linear extrapolation of glucose values. In view of the above, it is therefore, from a medical device point of view, desirable to develop a different approach to glucose prediction.
A good prediction of near future glucose levels is strongly desirable since it will enable the user to be alerted to potentially dangerous situations well in advance of any events happening, and to perform preventive actions to avoid spending too much time outside normoglycaemia. This could in turn further reduce, or perhaps even eliminate, the need for painful manual blood glucose check measurements.